MONITORING MODEL OF LAND COVER CHANGE FOR THE INDICATION OF DEVEGETATION AND REVEGETATION USING SENTINEL-2

IInformation on land cover change is very important for various purposes, including the monitoring of changes for environmental sustainability. The objective of this study is to create a monitoring model of land cover change for the indication of devegetation and revegetation usingdata fromSentinel-2 from 2017 to 2018 of the Brantas watershed.This is one of the priority watersheds in Indonesia, so it is necessary to observe changes in its environment, including land cover change. Such change can be detected using remote sensing data. The method used is a hybrid between Normalized Difference Vegetation Index(NDVI) and Normalized Burn Ratio (NBR) which aims to detect land changes with a focus on devegetationand revegetation by determining the threshold value for vegetation index (ΔNDVI) and open land index (ΔNBR).The study found that the best thresholds to detect revegetation were ΔNDVI > 0.0309 and ΔNBR 0.0314.It is concluded that Sentinel-2 data can be used to monitor land changes indicating devegetation and revegetation with established NDVI and NBR threshold conditions

IDENTIFICATION AND CLASSIFICATION OF FOREST TYPES USING DATA LANDSAT 8 IN KARO, DAIRI, AND SAMOSIR DISTRICTS, NORTH SUMATRA

Forests have important roles in terms of carbon storage and other values. Various studies have been conducted to identify and distinguish the forest from non-forest classes. Several forest types classes such as secondary forests and plantations should be distinguished related to the restoration and rehabilitation program for dealing with climate change. The study was carried out to distinguish several classes of important forests such as the primary dryland forests, secondary dryland forest, and plantation forests using Landsat 8 to develop identification techniques of specific forests classes. The study areas selected were forest areas in three districts, namely Karo, Dairi, and Samosir of North Sumatera Province. The results showed that using composite RGB 654 of Landsat 8 imagery based on test results OIF for the forest classification, the forests could be distinguished with other land covers. Digital classification can be combined with the visual classification known as a hybrid classification method, especially if there are difficulties in border demarcation between the two types of forest classes or two classes of land covers

DETERMINATION OF FOREST AND NON-FOREST IN SERAM ISLAND MALUKU PROVINCE USING MULTI-YEAR LANDSAT DATA

Seram Island is one of the islands in Maluku Province. Forest in Seram Island still exists because there is Manusela National Park, but they should be monitored. The forest and non-forest information is usually obtained through the classification process from single remote sensing data, but in certain places in Indonesia it is difficult enough to get  single Landsat data with cloud free, so annual mosaic was used. The aim of this research was to analyze the stratification zone, their indices and thresholds to get spatial information of annual forest area in Seram Island using multi-year Landsat Data. The method consists of four stages: 1) analyzing the base probability result for determination of stratification zone 2) determining the annual forest probability by applying indices from stage-I, 3) determining the spatial information of forest and non-forest annual phase-I by searching the lowest boundary of forest probability, and 4) determining the spatial information of forest and non-forest annual phase-II using the method of permutation of three data and multi-year forest rules. The results of this study indicated that Seram Island  could be coumpond into one stratification zone with three indices. The index equations were B2+B3-2B for index-1, B3+B4 for index-2, and -B3+B4 for index-3.   The threshold  of  index 1, 2, and 3 ranged between -60 and 0, 61 and 104, and 45 and 105, respectively. The lowest boundary  of forest probability in Seram Island since 2006 to 2012 have a range between 46% and 60%. The last result was the annual forest spatial information phase II where the missing data on the forest spatial information phase I decreased. The information is very important to analyze forest area change, especially in Seram Island.

DETERMINATION OF STRATIFICATION BOUNDARY FOR FOREST AND NON FOREST MULTITEMPORAL CLASSIFICATION TO SUPPORT REDD+ IN SUMATERA ISLAN

Multi-temporal classification is a method to determine forest and non-forest by considering a missing data, such as cloud cover using correlations value from the other data. This circumstances is frequently occured in a tropical area such as in Indonesia. To gain an optimum result of forest and non-forest classification, it is needed a stratification zone that describes the difference of vegetation condition due to different of vegetation type, soil type, climate, and land use/cover associations. This stratification zone will be useful to indicate the different biomass volume relating to carbon content for supporting the REDD+ project. The objective of this study was to determine stratification boundary by performing multi temporal  classification in Sumatera Island  using  Landsat  imagery  in  25 meter resolution and Quick Bird imagery in 0.6 meter. Rough stratification was made by considering land use/cover, DEM and landform, using visual interpretation of moderate spatial resolution of satellitedata. High spatial resolution data was also provided in some areas to increase the accuracy level of stratification zone. The stratification boundary was evaluated using forest classification indices, and it was  redetermined  to  obtain  the  final  stratification  zone. The  indices was generated  by CanonicalVariate Analysis (CVA) method, which was depend on training samples of forest and non-forest in each previous stratification zone. The amount of indices used in each zone were two or three indices depending on the separability of the forest and non-forest classification. The suitable indices used in each  zone  described forest  as  100, non-forest  as  0, and  uncertain  forest between  50-99. The  result showed 20 stratification zones in Sumatera spreading out in coastal, mountain, flat area, and group of small islands. The stratification zone will improve the accuracy of forest and non-forest classification result and their change based on multi temporal classification

Nilai-nilai budaya dalam kehidupan pesantren di daerah Situbondo Jawa Timur

Lembaga pesantren yang dipilih sebagai obyek penelitian adalah Pondok Pesantren Salafiyah Syafi'iyah, atau juga dikenal dengan sebutan Pondok Pesantren Sukorajo Asembagus yang berlokasi di Dusun Sukorejo, Kelurahan Sumberejo, Kecamatan Banyuputih , Kabupaten Situbodo, Propinsi Jawa Timur. Hasil penelitian pengkajian ini diharapkan dapat mengungkapkan potensi-potensi positif yang dimiliki oleh lembaga pesantren dalam membina dan mendidik para santrinya. Potensi-potensi ini diharapkan dapat menunjang peningkatan kualitas manusia Indonesia , yang tidak hanya handal dalam penguasaan ilmu pengetahuan dan teknologi, melainkan juga memiliki sikap dan kepribadian serta mentalitas yang luhur, sesuai dengan ajaran agama Islam. Selain itu, dari hasil penelitian ini diharapkan dapat memberikan masukan bagi pihak-pihak atau instansi yang secara langsung maupun tidak langsung terkait dalam upaya pengembangkan pendidikan formal maupun nonformal. Adapun tujuan kuusus dari penelitian ini adalah untuk menjawab pokok-pomok permasalahan yang telah diuraikan di atas, dalam rangka menyebarluaskan informal mengenai nilai-nilai budaya yang terdepat di dawlam masyarakat, khususnya di lingkungan pesantren

PENGUATAN DIGITALISASI UMKM MELALUI APLIKASI CIAMIK

Asosiasi Muslimah Pengusaha (ALISA) Khadija merupakan komunitas muslimah pengusaha di bawah organisasi Ikatan Cendekiawan Muslim Indonesia (ICMI). Mayoritas anggota ALISA Khadija adalah pengusaha dengan kriteria usaha mikro, dan sebagian lagi pada kriteria usaha kecil. Beberapa pengusaha ALISA Khadijah ICMI mengalami kendala dalam penyusunan laporan keuangan standar karena minimnya keahlian pengelolaan keuangan usaha. Kegiatan pengabdian masyarakat (PKM) ini bertujuan untuk menghasilkan luaran berupa aplikasi sistem akuntansi sederhana yang bernama aplikasi “CIAMIK”. Aplikasi ini diharapkan akan mempermudah anggota ALISA Khadijah ICMI DIY dalam proses penyusunan laporan keuangan, serta memberikan informasi yang akurat dalam pengembangan usaha. Program PKM ini bekerjasama dengan Asosiasi Muslimah Pengusaha (ALISA) Khadija DIY. Kegiatan PKM ini dilaksanakan dalam bentuk pelatihan dan pendampingan secara berkala tentang pengoperasian aplikasi akuntansi, analisis laporan keuangan, dan pengelolaan keuangan bagi UMKM. Pelaksanaan kegiatan pendampingan ini memberikan pemahaman bagi anggota ALISA Khadijah ICMI DIY untuk dapat mengoperasikan dan menerapkan aplikasi CIAMIK untuk membantu proses bisnis usaha

Vegetation height estimation using satellite remote sensing in peat land of Central Kalimantan

Vegetation height is an important parameter in monitoring peatlands. Vegetation height can be estimated using remote sensing. Vegetation height can be estimated by utilizing DSM and DTM. The data that can be used are LiDAR, X-SAR, and SRTM C. In this study, LiDAR data is used for DSM2018 and DTM2018 extraction. The purpose of this research is to detect the vegetation height in Central Kalimantan peatlands using remote sensing technology. The research location is in Bakengbongkei, Kalampangan, Central Kalimantan. The integration of X-SAR and SRTM C is used for DSM2000 and DTM2000 extraction. DSM2000, DTM2000, DSM2018, and DTM2018 performed height error correction with tolerance of 1.96? (95%). Then do the geoid undulation correction to EGM2008. The results obtained are DSM and DTM with a similar height reference field. If it meets these conditions it can be calculated the vegetation height estimation. Vegetation height can be obtained using the Differential DEM method. The Changing in vegetation height from 2000 to 2018 can be estimated from the difference in vegetation height from 2000 to vegetation height in 2018. Results of spatial information on vegetation height and its changes need to be tested for the accuracy. This accuracy-test includes a cross section test, height difference test, and comparison with measurements of vegetation height in the field. The results of this research can be used to monitor the changing the vegetation height in peatlands.Vegetation height is an important parameter in monitoring peatlands. Vegetation height can be estimated using remote sensing. Vegetation height can be estimated by utilizing DSM and DTM. The data that can be used are LiDAR, X-SAR, and SRTM C. In this study, LiDAR data is used for DSM2018 and DTM2018 extraction. This research aims to detect the vegetation height in Central Kalimantan peatlands using remote sensing technology. The research location is in Bakengbongkei, Kalampangan, Central Kalimantan. The integration of X-SAR and SRTM C is used for DSM2000 and DTM2000 extraction. DSM2000, DTM2000, DSM2018, and DTM2018 performed height error correction with tolerance of 1.96? (95%). Then do the geoid undulation correction to EGM2008. The results obtained are DSM and DTM with a similar height reference field. If it meets these conditions, it can be calculated the vegetation height estimation. Vegetation height can be obtained using the Differential DEM method. The Changing in vegetation height from 2000 to 2018 can be estimated from the difference in vegetation height from 2000 to vegetation height in 2018. Results of spatial information on vegetation height and its changes need to be tested for accuracy. This accuracy-test includes a cross-section test, height difference test, and comparison with vegetation height measurements in the ground. The results of this research can be used to monitor the changing vegetation height in peatlands

APLIKASI MODEL GEOBIOFISIK NDVI UNTUK IDENTIFIKASI HUTAN PADA DATA SATELIT LAPAN-A3

ABSTRAKSatelit LAPAN-A3/IPB merupakan satelit mikro yang dibuat anak bangsa dalam rangka membangun kemandirian bangsa bidang Keantariksaaan. Satelit ini memiliki 4 band diantaranya adalah 3 gelombang tampak dan 1 inframerah dekat. Mengingat  merupakan satelit baru, perlu dilakukan kajian dan penelitian terhadap kemampuan karakteristik sensor untuk mengidentifikasi sumberaya alam, salah satunya hutan. Pada penelitian ini selain menggunakan data satelit LAPAN-A3, juga digunakan data Landsat-8 sebagai data pembanding untuk pengujian tingkat akurasi ketelitian. Penentuan ekstraksi parameter geobiofisik identifikasi hutan menggunakan model Normalized Difference Vegetation Index (NDVI) dengan nilai ambang batas untuk identifikasi hutan.  Hasil penelitian dengan data satelit LAPAN-A3 menujukkan bahwa kisaran ambang batas untuk indentifikasi hutan adalah di atas 0,65 pada  skala indeks vegetasi -1 (minus satu) sampai +1 (plus satu), dengan tangkat akurasi 60% setelah dibandingkan dengan nilai NDVI pada data Landsat-8

EVALUASI REHABILITASI LAHAN KRITIS BERDASARKAN TREND NDVI LANDSAT-8 (Studi Kasus: DAS Serayu Hulu)

Pemanfaatan penginderaan jauh dalam memantau vegetasi sudah banyak dilakukan, tetapi pemanfaatannya untuk mengevaluasi rehabilitasi di lahan kritis masih sangat jarang. Kegiatan rehabiliatsi hutan dan lahan dilakukan karena makin meningkatnya lahan kritis. Kegiatan rehabilitasi tersebut perlu dievaluasi, mengingat banyak sekali dana, waktu, dan tenaga yang diperlukan. Selama ini evaluasi dilakukan dengan cara langsung mendatangi lokasi rehabilitasi dengan memantau pertumbuhan tanaman pada setiap akhir tahun sampai akhir tahun ketiga. Menurut ketentuan peraturan yang berlaku, rehabilitasi dapat dikatakan berhasil apabila 90% vegetasi yang ditanam bisa tumbuh di akhir tahun ketiga. Kegiatan evaluasi dengan cara memantau kondisi vegetasi atau kerapatannya dapat dilaksanakan dengan memanfaatkan data penginderaan jauh, karena data tersebut mempunyai sifat multi temporal dan cakupan yang luas dan ketersediannya yang berlimpah dan mudah didapat. Data penginderaan jauh yang digunakan adalah Landsat-8 tahun 2013 sampai dengan 2018 dan metode evaluasi adalah analisis NDVI dari waktu ke waktu menggunakan SIG. Hasilnya adalah bahwa dari hasil survey yang diperoleh di kawasan APL terdapat lokasi rehabilitasi di lahan tidak kritis, agak kritis, kritis, dan sangat kritis dan berturut-turut keberhasilan rehabilitasi untuk APL_TK; APL_K; APL_AK; APL_SK jika NDVI melampaui nilai 0,337; 0,465; 0,493; 0,490 setelah bulan ke 21,8; 24,5; 26, dan 25,8

KAJIAN KOREKSI TERRAIN PADA CITRA LANDSAT THEMATIC MAPPER (TM)

Terrain correction is used to minimize the shadow effect due to variation of earth`s topography. So, the process is very useful to correct the distortion of the pixel value at the mountainous area in the satellite image. The aim of this paper is to study the terrain correction process and its implementation for Landsat TM. The algoritm of the terrain correction was built by determining the pixel normal angle which is defined as an angle between the sun and surface normal directions. The calculation of the terrain correction needs the information of sun zenith angle, sun elevation angle (obtained from header data), pixel slope, and pixel aspect derived from digital elevation model (DEM). The C coefficient from each band was determined by calculating the gradient and the intercept of the correlation between the Cos pixel normal angle and the pixel reflectance in each band. Then, the Landsat TM image was corrected by the algorithm using the pixel normal angle and C coefficient. C Coefficients used in this research were obtained from our calculation and from Indonesia National Carbon Accounting System (INCAS). The result shows that without the C coefficient, pixels value increases very high when the pixel normal angle approximates 900. The C coefficient prevents that condition, so the implementation of the C coefficient obtained from INCAS in the algorithm can produce the image which has the same topography appearance. Further, each band of the corrected image has a good correlation with the corrected band from the INCAS result. The implementation of the C coefficient from our calculation still needs some evaluation, especially for the method to determine the training sample for calculating the C coefficient. Keywords: Terrain correction, Pixel normal angle, C coefficient, Landsat T